Search

This study bridges gaps in air pollution research by examining exposure dynamics in disadvantaged communities. Using cutting-edge machine learning and massive data processing, we produced high-resolution (100 meters) daily air pollution maps for nitrogen dioxide (NO 2 ), fine particulate matter (PM 2.5 ), and ozone (O 3 ) across California for 2012-2019. Our findings revealed opposite spatial patterns of NO 2 and PM 2.5 to that of O 3 . We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019, although the most disadvantaged communities saw the largest NO 2 and PM 2.5 reductions and the advantaged neighborhoods experienced greatest rising O 3 concentrations. Further, day-to-day exposure variations decreased for NO 2 and O 3 . The disparity in NO 2 exposure decreased, while it persisted for O 3 . In addition, PM 2.5 showed increased day-to-day variations across all communities due to the increase in wildfire frequency and intensity, particularly affecting advantaged suburban and rural communities.

In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM 2.5 ), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM 2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM 2.5 estimates. We used a concentration-response function for PM 2.5 , applying ZIP code-level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM 2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.

Fine particulate air pollution <2.5 μm in diameter (PM 2.5 ) is a major environmental threat to global public health. Multiple national and international medical and governmental organizations have recognized PM 2.5 as a risk factor for cardiopulmonary diseases. A growing body of evidence indicates that several personal-level approaches that reduce exposures to PM 2.5 can lead to improvements in health endpoints. Novel and forward-thinking strategies including randomized clinical trials are important to validate key aspects (e.g., feasibility, efficacy, health benefits, risks, burden, costs) of the various protective interventions, in particular among real-world susceptible and vulnerable populations. This paper summarizes the discussions and conclusions from an expert workshop, Reducing the Cardiopulmonary Impact of Particulate Matter Air Pollution in High Risk Populations, held on May 29 to 30, 2019, and convened by the National Institutes of Health, the U.S. Environmental Protection Agency, and the U.S. Centers for Disease Control and Prevention., Competing Interests: Author Disclosures The views expressed in this paper are those of the authors and do not necessarily represent the views of the National Institutes of Health (NIH), National Heart, Lung, and Blood Institute (NHLBI), National Institute of Environmental Health Sciences, Environmental Protection Agency, Centers for Disease Control and Prevention, or the United States Department of Health and Human Services. Drs. Bhatt and Brooks served as co-chairs of this NIH/NHLBI/EPA/CDC Expert Workshop on air pollution. Dr. Newman has received grant support from the NIH (K23HL125991). Dr. Bhatt has served on the advisory board of Cardax, CellProthera, Cereno Scientific, Elsevier Practice Update Cardiology, Level Ex, Medscape Cardiology, PhaseBio, PLx Pharma, and Regado Biosciences; has served on the Board of Directors for Boston VA Research Institute, Society of Cardiovascular Patient Care, and TobeSoft; has served as chair of the American Heart Association Quality Oversight Committee, NCDR-ACTION Registry Steering Committee, and VA CART Research and Publications Committee; has served on the data monitoring committees of Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute, for the PORTICO trial, funded by St. Jude Medical, now Abbott), Cleveland Clinic (including for the ExCEED trial, funded by Edwards), Contego Medical (Chair, PERFORMANCE 2), Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine (for the ENVISAGE trial, funded by Daiichi-Sankyo), and the Population Health Research Institute; has received honoraria from the American College of Cardiology (Senior Associate Editor, Clinical Trials and News, ACC.org; Vice-Chair, ACC Accreditation Committee), Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute; RE-DUAL PCI clinical trial steering committee funded by Boehringer Ingelheim; AEGIS-II executive committee funded by CSL Behring), Belvoir Publications (Editor-in-Chief, Harvard Heart Letter), Duke Clinical Research Institute (clinical trial steering committees, including for the PRONOUNCE trial, funded by Ferring Pharmaceuticals), HMP Global (Editor-in-Chief, Journal of Invasive Cardiology), Journal of the American College of Cardiology (Guest Editor; Associate Editor), K2P (Co-Chair, interdisciplinary curriculum), Level Ex, Medtelligence/ReachMD (CME steering committees), MJH Life Sciences, Population Health Research Institute (for the COMPASS operations committee, publications committee, steering committee, and USA national co-leader, funded by Bayer), Slack Publications (Chief Medical Editor, Cardiology Today’s Intervention), Society of Cardiovascular Patient Care (Secretary/Treasurer), and WebMD (CME steering committees); has served as Deputy Editor of Clinical Cardiology; has received research funding from Abbott, Afimmune, Amarin, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardax, Chiesi, CSL Behring, Eisai, Ethicon, Ferring Pharmaceuticals, Forest Laboratories, Fractyl, Idorsia, Ironwood, Ischemix, Lexicon, Lilly, Medtronic, Pfizer, PhaseBio, PLx Pharma, Regeneron, Roche, Sanofi, Synaptic, and The Medicines Company; has received royalties from Elsevier (Editor, Cardiovascular Intervention: A Companion to Braunwald’s Heart Disease); has served as site co-investigator for Biotronik, Boston Scientific, CSI, St. Jude Medical (now Abbott), and Svelte; served as a trustee of the American College of Cardiology; and has received unfunded research from FlowCo, Merck, Novo Nordisk, and Takeda. Dr. Balmes is the Physician Member of the California Air Resources Board. Dr. Brook has received an investigator-initiated grant from RB, Inc.; and has received support from NIH awards 2R01-ES019616 and 2R01-NR014484. Dr. Brauer has received grant support from the Health Effects Institute, the Canadian Institutes of Health Research, The Terry Fox Research Institute, and the Wellcome Trust. Dr. Hansel has received grant support from the NIH, EPA, NHLBI, National Institute of Environmental Health Sciences, COPD Foundation, and Boehringer Ingelheim; and has participated in advisory boards for GlaxoSmithKline, AstraZeneca, and Mylan. Dr. Hernandez has received research funding from American Regent, AstraZeneca, Boehringer Ingelheim, Merck, Novartis, and Verily; and has served as a consultant for Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Boston Scientific, Merck, Novartis, and Relypsa. Dr. Hochman has received grant support from the NHLBI for the ISCHEMIA Trial, for which there were in-kind donations for participating sites from Abbott Vascular, Medtronic, Inc., St. Jude Medical, Inc., Volcano Corporation, Arbor Pharmaceuticals, AstraZeneca Pharmaceuticals, Merck Sharp & Dohme Corp., Omron Healthcare, Inc., and Amgen Inc; has received financial donations from Arbor Pharmaceuticals and AstraZeneca Pharmaceuticals; and has served as PI for the ZEDS (Long-term Suppressive Valacyclovir Treatment for Herpes Zoster Ophthalmicus) Study, supported by NIH/National Eye Institute. Dr. Kaufman has received grant support from the National Institutes of Health and the Environmental Protection Agency. Dr. Malik is supported by the NHLBI of the NIH under Award Number T32HL110837. Dr. Peel has received research funding from the NIH (1UM1HL134590 and R01ES023688). Dr. Rajagopalan has received research funding from the NIH; and has served as a consultant to Novo Nordisk, Takeda, GlaxoSmithKline, and AstraZeneca. Dr. Zhang’s research has been partly funded by the NIH, Underwriter’s Laboratory, and Natural Science Foundation of China (via Duke Kunshan University in China). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2020 American College of Cardiology Foundation. All rights reserved.)

Background: Ambient air pollution is a modifiable risk factor for cardiovascular disease, yet uncertainty remains about the size of risks at lower levels of fine particulate matter (PM2.5) exposure which now occur in the USA and elsewhere., Methods: We investigated the relationship of ambient PM2.5 exposure with cause-specific cardiovascular disease mortality in 565 477 men and women, aged 50 to 71 years, from the National Institutes of Health-AARP Diet and Health Study. During 7.5 x 106 person-years of follow up, 41 286 cardiovascular disease deaths, including 23 328 ischaemic heart disease (IHD) and 5894 stroke deaths, were ascertained using the National Death Index. PM2.5 was estimated using a hybrid land use regression (LUR) geostatistical model. Multivariate Cox regression models were used to estimate relative risks (RRs) and 95% confidence intervals (CI)., Results: Each increase of 10  μg/m3 PM2.5 (overall range, 2.9-28.0  μg/m3) was associated, in fully adjusted models, with a 16% increase in mortality from ischaemic heart disease [hazard ratio (HR) 1.16; 95% CI 1.09-1.22] and a 14% increase in mortality from stroke (HR 1.14; CI 1.02-1.27). Compared with PM2.5 exposure <8  μg/m3 (referent), risks for CVD were increased in relation to PM2.5 exposures in the range of 8-12  μg/m3 (CVD: HR 1.04; 95% CI 1.00-1.08), in the range 12-20  μg/m3 (CVD: HR 1.08; 95% CI 1.03-1.13) and in the range 20+ μg/m3 (CVD: HR 1.19; 95% CI 1.10-1.28). Results were robust to alternative approaches to PM2.5 exposure assessment and statistical analysis., Conclusions: Long-term exposure to fine particulate air pollution is associated with ischaemic heart disease and stroke mortality, with excess risks occurring in the range of and below the present US long-term standard for ambient exposure to PM2.5 (12  µg/m3), indicating the need for continued improvements in air pollution abatement for CVD prevention., (© The Author(s) 2019; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Exposure to air pollution increases cardiovascular morbidity and mortality. Preventing chronic cardiovascular diseases caused by air pollution relies on detecting the early effects of pollutants on the risk of cardiovascular disease development, which is limited by the lack of sensitive biomarkers. We have previously identified promising biomarkers in experimental animals but comparable evidence in humans is lacking., Methods: Air pollution is substantially worse in Beijing than in Los Angeles. We collected urine and blood samples from 26 nonsmoking, healthy adult residents of Los Angeles (mean age, 23.8 years; 14 women) before, during, and after spending 10 weeks in Beijing during the summers of 2014 and 2015. We assessed a panel of circulating biomarkers indicative of lipid peroxidation and inflammation. Personal exposure to polycyclic aromatic hydrocarbons (PAHs), a group of combustion-originated air pollutants, was assessed by urinary PAH metabolite levels., Results: Urinary concentrations of 4 PAH metabolites were 176% (95% CI, 103% to 276%) to 800% (95% CI, 509% to 1780%) greater in Beijing than in Los Angeles. Concentrations of 6 lipid peroxidation biomarkers were also increased in Beijing, among which 5-, 12-, and 15-hydroxyeicosatetraenoic acid and 9- and 13-hydroxyoctadecadienoic acid levels reached statistical significance (false discovery rate <5%), but not 8-isoprostane (20.8%; 95% CI, -5.0% to 53.6%). The antioxidative activities of paraoxonase (-9.8%; 95% CI, -14.0% to -5.3%) and arylesterase (-14.5%; 95% CI, -22.3% to -5.8%) were lower and proinflammatory C-reactive protein (101%; 95% CI, 3.3% to 291%) and fibrinogen (48.3%; 95% CI, 4.9% to 110%) concentrations were higher in Beijing. Changes in all these biomarkers were reversed, at least partially, after study participants returned to Los Angeles. Changes in most outcomes were associated with urinary PAH metabolites ( P <0.05)., Conclusions: Traveling from a less-polluted to a more-polluted city induces systemic pro-oxidative and proinflammatory effects. Changes in the levels of 5-, 12-, and 15-hydroxyeicosatetraenoic acid and 9- and 13-hydroxyoctadecadienoic acid as well as paraoxonase and arylesterase activities in the blood, in association with exposures to PAH metabolites, might have important implications in preventive medicine as indicators of increased cardiovascular risk caused by air pollution exposure.

Limited research has been conducted on the contributions of local and nonlocal emission sources to ambient fine particulate matter (PM 2.5 ) and ozone (O 3 ) and their associated mortality. In this study, we estimated the total mortality resulting from long-term PM 2.5 and O 3 exposures in California in 2012 using multiple concentration response functions (CRFs) and attributed the estimated mortality to different emission groups. The point estimates of PM 2.5 -associated mortality in California ranged from 12,700 to 26,700, of which 53% were attributable to in-state anthropogenic emissions. Based on new epidemiological evidence, we estimated that O 3 could be associated with up to 13,700 deaths from diseases of both the respiratory and cardiovascular systems in California. In addition, 75% of the ambient O 3 in California was due to distant emissions outside the western United States, leading to 92% of the O 3 -associated mortality. Overall, distant emissions lead to greater mortality burdens of air pollution in California than local anthropogenic emissions., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In October of 2015, a large underground storage well at the Aliso Canyon natural gas storage facility experienced a massive methane leak (also referred to as "natural gas blowout"), which resulted in the largest ever anthropogenic release of methane from a single point source in the United States. Additional sampling conducted during the event revealed unique gas and particle concentrations in ambient air and a characteristic "fingerprint" of metals in the indoor dust samples similar to samples taken at the blowout site. We further investigated the association between the Aliso Canyon natural gas storage site and several measured air pollutants by: (a) conducting additional emission source studies using meteorological data and correlations between particulate matter, methane, and hazardous air pollutants (HAPs) collected during the natural gas blowout at distances ranging from 1.2 to 7.3 km due south of well SS25, (b) identifying the unique i/n-pentane ratio signature associated with emissions from the blowout event, and (c) identifying characteristics unique to the homes that tested positive for air pollutants using data collected from extensive indoor environmental assessment surveys. Results of air quality samples collected near Aliso Canyon during the final weeks of the event revealed that elevated levels of several HAP compounds were likely influenced by the active natural gas blowout. Furthermore, the final attempts to plug the well during the days preceding the well kill were associated with particle emissions likely from the well site. Together, this investigation suggests uncontrolled leaks or blowout events at natural gas storage facilities have the potential to release harmful pollutants with adverse health and environmental consequences into proximate communities. With this evidence, our recommendations include facility-specific meteorological and air quality data-collection equipment installed at natural gas storage facilities and support of environmental surveillance after severe off-normal operation events., (Copyright © 2019. Published by Elsevier Ltd.)

Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organizations, academics, or government agencies. Networks of many community monitors have the potential to fill the spatial gaps between existing government-operated monitoring sites. One potential benefit of finer scale monitoring might be the ability to discern elevated air pollution episodes in locations that have not been identified by government-operated monitoring sites, which might improve public health warnings for populations sensitive to high levels of air pollution. In the Imperial Air study, a large network of low-cost particle monitors was deployed in the Imperial Valley in Southeastern California. Data from the new monitors is validated against regulatory air monitoring. Neighborhood-level air pollution episodes, which are defined as periods in which the PM 2.5 (airborne particles with sizes less than 2.5 μm in diameter) hourly average concentration is equal to or greater than 35 μg m -3 , are identified and corroborate with other sites in the network and against the small number of government monitors in the region. During the period from October 2016 to February 2017, a total of 116 episodes were identified among six government monitors in the study region; however, more than 10 times as many episodes are identified among the 38 community air monitors. Of the 1426 episodes identified by the community sensors, 723 (51%) were not observed by the government monitors. These findings suggest that the dense network of community air monitors could be useful for addressing current limitations in the spatial coverage of government air monitoring to provide real-time warnings of high pollution episodes to vulnerable populations., Competing Interests: The authors declare no conflict of interest.

Wildfires have been increasing in frequency in the western United States (US) with the 2017 and 2018 fire seasons experiencing some of the worst wildfires in terms of suppression costs and air pollution that the western US has seen. Although growing evidence suggests respiratory exacerbations from elevated fine particulate matter (PM 2.5 ) during wildfires, significantly less is known about the impacts on human health of ozone (O 3 ) that may also be increased due to wildfires. Using machine learning, we created daily surface concentration maps for PM 2.5 and O 3 during an intense wildfire in California in 2008. We then linked these daily exposures to counts of respiratory hospitalizations and emergency department visits at the ZIP code level. We calculated relative risks of respiratory health outcomes using Poisson generalized estimating equations models for each exposure in separate and mutually-adjusted models, additionally adjusted for pertinent covariates. During the active fire periods, PM 2.5 was significantly associated with exacerbations of asthma and chronic obstructive pulmonary disease (COPD) and these effects remained after controlling for O 3 . Effect estimates of O 3 during the fire period were non-significant for respiratory hospitalizations but were significant for ED visits for asthma (RR = 1.05 and 95% CI = (1.022, 1.078) for a 10 ppb increase in O 3 ). In mutually-adjusted models, the significant findings for PM 2.5 remained whereas the associations with O 3 were confounded. Adjusted for O 3 , the RR for asthma ED visits associated with a 10 μg/m 3 increase in PM 2.5 was 1.112 and 95% CI = (1.087, 1.138). The significant findings for PM 2.5 but not for O 3 in mutually-adjusted models is likely due to the fact that PM 2.5 levels during these fires exceeded the 24-hour National Ambient Air Quality Standard (NAAQS) of 35 μg/m 3 for 4976 ZIP-code days and reached levels up to 6.073 times the NAAQS, whereas our estimated O 3 levels during the fire period only occasionally exceeded the NAAQS of 70 ppb with low exceedance levels. Future studies should continue to investigate the combined role of O 3 and PM 2.5 during wildfires to get a more comprehensive assessment of the cumulative burden on health from wildfire smoke., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Although long-term exposure to traffic-related air pollutants such as nitrogen dioxide has been linked to cardiovascular disease (CVD) mortality, little is known about the association between ultrafine particles (UFPs), defined as particles less than or equal to 0.1 μm in diameter, and incidence of major CVD events. We conducted a population-based cohort study to assess the associations of chronic exposure to UFPs and nitrogen dioxide with incident congestive heart failure (CHF) and acute myocardial infarction. Our study population comprised all long-term Canadian residents aged 30-100 years who lived in Toronto, Ontario, Canada, during the years 1996-2012. We estimated annual concentrations of UFPs and nitrogen dioxide by means of land-use regression models and assigned these estimates to participants' postal-code addresses in each year during the follow-up period. We estimated hazard ratios for the associations of UFPs and nitrogen dioxide with incident CVD using random-effects Cox proportional hazards models. We controlled for smoking and obesity using an indirect adjustment method. Our cohorts comprised approximately 1.1 million individuals at baseline. In single-pollutant models, each interquartile-range increase in UFP exposure was associated with increased incidence of CHF (hazard ratio for an interquartile-range increase (HRIQR) = 1.03, 95% confidence interval (CI): 1.02, 1.05) and acute myocardial infarction (HRIQR = 1.05, 95% CI: 1.02, 1.07). Adjustment for fine particles and nitrogen dioxide did not materially change these estimated associations. Exposure to nitrogen dioxide was also independently associated with higher CHF incidence (HRIQR = 1.04, 95% CI: 1.03, 1.06).

Exposure to ambient fine particulate matter (PM 2.5 ) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM 2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM 2.5 -mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM 2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM 2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations., Competing Interests: Conflict of interest statement: J.V.S. is an independent consultant and not benefiting commercially from the results of this research., (Copyright © 2018 the Author(s). Published by PNAS.)

Background: Previous studies reported that long-term exposure to traffic-related air pollution may increase the incidence of hypertension and diabetes. However, little is known about the associations of ultrafine particles (≤0.1 μm in diameter) with these two conditions., Methods: We conducted a population-based cohort study to investigate the associations between exposures to ultrafine particles and nitrogen dioxide (NO2) and the incidence of diabetes and hypertension. Our study population included all Canadian-born residents aged 30 to 100 years who lived in the City of Toronto, Canada, from 1996 to 2012. Outcomes were ascertained using validated province-wide databases. We estimated annual concentrations of ultrafine particles and NO2 using land-use regression models and assigned these estimates to participants' annual postal code addresses during the follow-up period. Using random-effects Cox proportional hazards models, we calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for ultrafine particles and NO2, adjusted for individual- and neighborhood-level covariates. We considered both single- and multipollutant models., Results: Each interquartile change in exposure to ultrafine particles was associated with increased risk of incident hypertension (HR = 1.03; 95% CI = 1.02, 1.04) and diabetes (HR = 1.06; 95% CI = 1.05, 1.08) after adjusting for all covariates. These results remained unaltered with further control for fine particulate matter (≤2.5 μm; PM2.5) and NO2. Similarly, NO2 was positively associated with incident diabetes (HR = 1.06; 95% CI = 1.05, 1.07) after controlling for ultrafine particles and PM2.5., Conclusions: Exposure to traffic-related air pollution including ultrafine particles and NO2 may increase the risk for incident hypertension and diabetes. See video abstract at, http://links.lww.com/EDE/B337.

Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach., Competing Interests: The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM 2.5 and PM 10 (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R 2 for converted hourly averaged Dylos mass measurements versus a PM 2.5 BAM was 0.79 and that versus a PM 10 BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM 2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R 2 = 0.35-0.81)., Implications: The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.

Summary: The Imperial County Community Air Monitoring Network (the Network) is a collaborative group of community, academic, nongovernmental, and government partners designed to fill the need for more detailed data on particulate matter in an area that often exceeds air quality standards. The Network employs a community-based environmental monitoring process in which the community and researchers have specific, well-defined roles as part of an equitable partnership that also includes shared decision-making to determine study direction, plan research protocols, and conduct project activities. The Network is currently producing real-time particulate matter data from 40 low-cost sensors throughout Imperial County, one of the largest community-based air networks in the United States. Establishment of a community-led air network involves engaging community members to be citizen-scientists in the monitoring, siting, and data collection process. Attention to technical issues regarding instrument calibration and validation and electronic transfer and storage of data is also essential. Finally, continued community health improvements will be predicated on facilitating community ownership and sustainability of the network after research funds have been expended. https://doi.org/10.1289/EHP1772

Background: Little is known about the long-term health effects of ambient ultrafine particles (<0.1 μm) (UFPs) including their association with respiratory disease incidence. In this study, we examined the relationship between long-term exposure to ambient UFPs and the incidence of lung cancer, adult-onset asthma, and chronic obstructive pulmonary disease (COPD)., Methods: Our study cohort included approximately 1.1 million adults who resided in Toronto, Canada and who were followed for disease incidence between 1996 and 2012. UFP exposures were assigned to residential locations using a land use regression model. Random-effect Cox proportional hazard models were used to estimate hazard ratios (HRs) describing the association between ambient UFPs and respiratory disease incidence adjusting for ambient fine particulate air pollution (PM 2.5 ), NO 2 , and other individual/neighbourhood-level covariates., Results: In total, 74,543 incident cases of COPD, 87,141 cases of asthma, and 12,908 cases of lung cancer were observed during follow-up period. In single pollutant models, each interquartile increase in ambient UFPs was associated with incident COPD (HR = 1.06, 95% CI: 1.05, 1.09) but not asthma (HR = 1.00, 95% CI: 1.00, 1.01) or lung cancer (HR = 1.00, 95% CI: 0.97, 1.03). Additional adjustment for NO 2 attenuated the association between UFPs and COPD and the HR was no longer elevated (HR = 1.01, 95% CI: 0.98, 1.03). PM 2.5 and NO 2 were each associated with increased incidence of all three outcomes but risk estimates for lung cancer were sensitive to indirect adjustment for smoking and body mass index., Conclusions: In general, we did not observe clear evidence of positive associations between long-term exposure to ambient UFPs and respiratory disease incidence independent of other air pollutants. Further replication is required as few studies have evaluated these relationships.

Background: Ozone is a ubiquitous air pollutant with increasing concentrations in many populous regions. Toxicological studies show that ozone can cause oxidative stress and increase insulin resistance. These pathways may contribute to metabolic changes and diabetes formation. In this paper, we investigate the association between ozone and incident type 2 diabetes in a large cohort of African American women., Methods: We used Cox proportional hazards models to calculate hazard ratios (HRs) for incident type 2 diabetes associated with exposure to ozone in a cohort of 45,231 African American women living in 56 metropolitan areas across the United States. Ozone levels were estimated using the U.S. EPA Models-3/Community Multiscale Air Quality (CMAQ) predictions fused with ground measurements at a resolution of 12km for the years 2007-2008., Results: The HR per interquartile range increment of 6.7ppb of ozone was 1.18 (95% CI 1.04-1.34) for incident diabetes in adjusted models. This association was unaltered in models that controlled for fine particulate matter with diameter <2.5μm (PM 2.5 ). Associations were modified by nitrogen dioxide (NO 2 ) levels, such that HRs for ozone levels were larger in areas of lower NO 2 ., Conclusions: Our results provide initial evidence of a positive association between O 3 and incident diabetes in African American women. Given the ubiquity of ozone exposure and the importance of diabetes on quality of life and survival, these results may have important implications for the protection of public health., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Background: Remote sensing (RS) is increasingly used for exposure assessment in epidemiological and burden of disease studies, including those investigating whether chronic exposure to ambient fine particulate matter (PM 2.5 ) is associated with mortality., Objectives: We compared relative risk estimates of mortality from diseases of the circulatory system for PM 2.5 modeled from RS with that for PM 2.5 modeled using ground-level information., Methods: We geocoded the baseline residence of 668,629 American Cancer Society Cancer Prevention Study II (CPS-II) cohort participants followed from 1982 to 2004 and assigned PM 2.5 levels to all participants using seven different exposure models. Most of the exposure models were averaged for the years 2002-2004, and one RS estimate was for a longer, contemporaneous period. We used Cox proportional hazards regression to estimate relative risks (RRs) for the association of PM 2.5 with circulatory mortality and ischemic heart disease., Results: Estimates of mortality risk differed among exposure models. The smallest relative risk was observed for the RS estimates that excluded ground-based monitors for circulatory deaths [RR = 1.02, 95% confidence interval (CI): 1.00, 1.04 per 10 μg/m 3 increment in PM 2.5 ]. The largest relative risk was observed for the land-use regression model that included traffic information (RR = 1.14, 95% CI: 1.11, 1.17 per 10 μg/m 3 increment in PM 2.5 )., Conclusions: We found significant associations between PM 2.5 and mortality in every model; however, relative risks estimated from exposure models using ground-based information were generally larger than those estimated using RS alone.

Associations between long-term exposure to ambient fine particulate matter (PM 2.5 ) and all-cause and cardiovascular mortality are well documented however less is known regarding possible interactions with cigarette smoking. We previously reported a supra-additive synergistic relationship between PM 2.5 and cigarette smoking for lung cancer mortality. Here we examine interactions for all-cause and cardiovascular mortality among 429,406 current or never smoking participants in the prospective American Cancer Society Cancer Prevention Study-II with modeled PM 2.5 concentrations. Cox proportional and additive hazards models were used to estimate mortality associations and interactions on the multiplicative and additive scales. A total of 146,495 all-cause and 64,339 cardiovascular (plus diabetes) deaths were observed. The hazard ratio (HR) (95% confidence interval (CI)) for cardiovascular mortality for high vs. low PM 2.5 exposure (>14.44µg/m 3 vs ≤10.59µg/m 3 , 75th vs 25th percentile) was 1.09 (95% CI 1.05, 1.12) in never smokers. The HR for cigarette smoking was 1.89 (95% CI 1.82, 1.96) in those with low PM 2.5 . The HR for both high PM 2.5 and cigarette smoking was 2.08 (95% CI 2.00, 2.17). A small significant excess relative risk due to interaction (0.10; 95% CI 0.02, 0.19) was observed. Quantification of the public health burden attributed to the interaction between PM 2.5 and cigarette smoking indicated a total of 32 (95% CI -6, 71) additional cardiovascular deaths per 100,000 person-years due to this interaction. In conclusion, PM 2.5 was associated with all-cause and cardiovascular mortality in both smokers and never smokers, with some evidence for a small additive interaction with cigarette smoking. Reductions in cigarette smoking will result in the greatest impact on reducing all-cause and cardiovascular death at the levels of PM 2.5 observed in this study. However, reductions in PM 2.5 will also contribute to preventing a proportion of mortality attributed to cigarette smoking., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: Exceptional aging, defined as reaching age 85 years, shows geographic inequalities that may depend on local environmental conditions. Links between particulate pollution-a well-recognized environmental risk factor-and exceptional aging have not been investigated., Objectives: We conducted a nationwide analysis of ~28 million adults in 3,034 United States counties to determine whether local PM2.5 levels (particulate matter < 2.5 μm in aerodynamic diameter) affected the probability of becoming 85- to 94-year-olds or centenarians (100- to 104-year-olds) in 2010 for individuals who were 55-64 or 70-74 years old, respectively, in 1980., Methods: We used population-weighted regression models including county-level PM2.5 from hybrid land-use regression and geostatistical interpolation, smoking, obesity, sociodemographic, and age-specific migration variables., Results: On average, 2,295 and 71.4 per 10,000 of the 55- to 64- and 70- to 74-year-olds in 1980, respectively, remained in the 85- to 94- and 100- to 104-year-old population in 2010. An interquartile range (4.19 μg/m3) increase in PM2.5 was associated with 93.7 fewer 85- to 94-year-olds (p < 0.001) and 3.5 fewer centenarians (p < 0.05). These associations were nearly linear, were stable to model specification, and were detectable below the annual PM2.5 national standard. Exceptional aging was strongly associated with smoking, with an interquartile range (4.77%) increase in population who smoked associated with 181.9 fewer 85- to 94-year-olds (p < 0.001) and 6.4 fewer centenarians (p < 0.001). Exceptional aging was also associated with obesity rates and median income., Conclusions: Communities with the most exceptional aging have low ambient air pollution and low rates of smoking, poverty, and obesity. Improvements in these determinants may contribute to increasing exceptional aging. Citation: Baccarelli AA, Hales N, Burnett RT, Jerrett M, Mix C, Dockery DW, Pope CA III. 2016. Particulate air pollution, exceptional aging, and rates of centenarians: a nationwide analysis of the United States, 1980-2010. Environ Health Perspect 124:1744-1750; http://dx.doi.org/10.1289/EHP197., Competing Interests: The authors declare they have no actual or potential competing financial interests.

We investigated health effects associated with fine particulate matter during a long-lived, large wildfire complex in northern California in the summer of 2008. We estimated exposure to PM2.5 for each day using an exposure prediction model created through data-adaptive machine learning methods from a large set of spatiotemporal data sets. We then used Poisson generalized estimating equations to calculate the effect of exposure to 24-hour average PM2.5 on cardiovascular and respiratory hospitalizations and ED visits. We further assessed effect modification by sex, age, and area-level socioeconomic status (SES). We observed a linear increase in risk for asthma hospitalizations (RR=1.07, 95% CI=(1.05, 1.10) per 5µg/m(3) increase) and asthma ED visits (RR=1.06, 95% CI=(1.05, 1.07) per 5µg/m(3) increase) with increasing PM2.5 during the wildfires. ED visits for chronic obstructive pulmonary disease (COPD) were associated with PM2.5 during the fires (RR=1.02 (95% CI=(1.01, 1.04) per 5µg/m(3) increase) and this effect was significantly different from that found before the fires but not after. We did not find consistent effects of wildfire smoke on other health outcomes. The effect of PM2.5 during the wildfire period was more pronounced in women compared to men and in adults, ages 20-64, compared to children and adults 65 or older. We also found some effect modification by area-level median income for respiratory ED visits during the wildfires, with the highest effects observed in the ZIP codes with the lowest median income. Using a novel spatiotemporal exposure model, we found some evidence of differential susceptibility to exposure to wildfire smoke., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Survivors of acute myocardial infarction (AMI) are at increased risk of dying within several hours to days following exposure to elevated levels of ambient air pollution. Little is known, however, about the influence of long-term (months to years) air pollution exposure on survival after AMI., Objective: We conducted a population-based cohort study to determine the impact of long-term exposure to fine particulate matter ≤ 2.5 μm in diameter (PM2.5) on post-AMI survival., Methods: We assembled a cohort of 8,873 AMI patients who were admitted to 1 of 86 hospital corporations across Ontario, Canada in 1999-2001. Mortality follow-up for this cohort extended through 2011. Cumulative time-weighted exposures to PM2.5 were derived from satellite observations based on participants' annual residences during follow-up. We used standard and multilevel spatial random-effects Cox proportional hazards models and adjusted for potential confounders., Results: Between 1999 and 2011, we identified 4,016 nonaccidental deaths, of which 2,147 were from any cardiovascular disease, 1,650 from ischemic heart disease, and 675 from AMI. For each 10-μg/m3 increase in PM2.5, the adjusted hazard ratio (HR10) of nonaccidental mortality was 1.22 [95% confidence interval (CI): 1.03, 1.45]. The association with PM2.5 was robust to sensitivity analyses and appeared stronger for cardiovascular-related mortality: ischemic heart (HR10 = 1.43; 95% CI: 1.12, 1.83) and AMI (HR10 = 1.64; 95% CI: 1.13, 2.40). We estimated that 12.4% of nonaccidental deaths (or 497 deaths) could have been averted if the lowest measured concentration in an urban area (4 μg/m3) had been achieved at all locations over the course of the study., Conclusions: Long-term air pollution exposure adversely affects the survival of AMI patients., Citation: Chen H, Burnett RT, Copes R, Kwong JC, Villeneuve PJ, Goldberg MS, Brook RD, van Donkelaar A, Jerrett M, Martin RV, Brook JR, Kopp A, Tu JV. 2016. Ambient fine particulate matter and mortality among survivors of myocardial infarction: population-based cohort study. Environ Health Perspect 124:1421-1428; http://dx.doi.org/10.1289/EHP185., Competing Interests: The opinions, results, and conclusions reported in this paper do not necessarily represent the views of the Institute for Clinical Evaluative Sciences or the Ministry of Health and Long-term Care. The authors declare they have no actual or potential competing financial interests.

Background: Fine particulate matter (PM2.5) air pollution exposure has been identified as a global health threat. However, the types and sources of particles most responsible are not yet known., Objectives: We sought to identify the causal characteristics and sources of air pollution underlying past associations between long-term PM2.5 exposure and ischemic heart disease (IHD) mortality, as established in the American Cancer Society's Cancer Prevention Study-II cohort., Methods: Individual risk factor data were evaluated for 445,860 adults in 100 U.S. metropolitan areas followed from 1982 through 2004 for vital status and cause of death. Using Cox proportional hazard models, we estimated IHD mortality hazard ratios (HRs) for PM2.5, trace constituents, and pollution source-associated PM2.5, as derived from air monitoring at central stations throughout the nation during 2000-2005., Results: Associations with IHD mortality varied by PM2.5 mass constituent and source. A coal combustion PM2.5 IHD HR = 1.05 (95% CI: 1.02, 1.08) per microgram/cubic meter, versus an IHD HR = 1.01 (95% CI: 1.00, 1.02) per microgram/cubic meter PM2.5 mass, indicated a risk roughly five times higher for coal combustion PM2.5 than for PM2.5 mass in general, on a per microgram/cubic meter PM2.5 basis. Diesel traffic-related elemental carbon (EC) soot was also associated with IHD mortality (HR = 1.03; 95% CI: 1.00, 1.06 per 0.26-μg/m3 EC increase). However, PM2.5 from both wind-blown soil and biomass combustion was not associated with IHD mortality., Conclusions: Long-term PM2.5 exposures from fossil fuel combustion, especially coal burning but also from diesel traffic, were associated with increases in IHD mortality in this nationwide population. Results suggest that PM2.5-mortality associations can vary greatly by source, and that the largest IHD health benefits per microgram/cubic meter from PM2.5 air pollution control may be achieved via reductions of fossil fuel combustion exposures, especially from coal-burning sources., Citation: Thurston GD, Burnett RT, Turner MC, Shi Y, Krewski D, Lall R, Ito K, Jerrett M, Gapstur SM, Diver WR, Pope CA III. 2016. Ischemic heart disease mortality and long-term exposure to source-related components of U.S. fine particle air pollution. Environ Health Perspect 124:785-794; http://dx.doi.org/10.1289/ehp.1509777.

Background: Outdoor fine particulate matter (≤ 2.5 μm; PM2.5) has been identified as a global health threat, but the number of large U.S. prospective cohort studies with individual participant data remains limited, especially at lower recent exposures., Objectives: We aimed to test the relationship between long-term exposure PM2.5 and death risk from all nonaccidental causes, cardiovascular (CVD), and respiratory diseases in 517,041 men and women enrolled in the National Institutes of Health-AARP cohort., Methods: Individual participant data were linked with residence PM2.5 exposure estimates across the continental United States for a 2000-2009 follow-up period when matching census tract-level PM2.5 exposure data were available. Participants enrolled ranged from 50 to 71 years of age, residing in six U.S. states and two cities. Cox proportional hazard models yielded hazard ratio (HR) estimates per 10 μg/m3 of PM2.5 exposure., Results: PM2.5 exposure was significantly associated with total mortality (HR = 1.03; 95% CI: 1.00, 1.05) and CVD mortality (HR = 1.10; 95% CI: 1.05, 1.15), but the association with respiratory mortality was not statistically significant (HR = 1.05; 95% CI: 0.98, 1.13). A significant association was found with respiratory mortality only among never smokers (HR = 1.27; 95% CI: 1.03, 1.56). Associations with 10-μg/m3 PM2.5 exposures in yearly participant residential annual mean, or in metropolitan area-wide mean, were consistent with baseline exposure model results. Associations with PM2.5 were similar when adjusted for ozone exposures. Analyses of California residents alone also yielded statistically significant PM2.5 mortality HRs for total and CVD mortality., Conclusions: Long-term exposure to PM2.5 air pollution was associated with an increased risk of total and CVD mortality, providing an independent test of the PM2.5-mortality relationship in a new large U.S. prospective cohort experiencing lower post-2000 PM2.5 exposure levels., Citation: Thurston GD, Ahn J, Cromar KR, Shao Y, Reynolds HR, Jerrett M, Lim CC, Shanley R, Park Y, Hayes RB. 2016. Ambient particulate matter air pollution exposure and mortality in the NIH-AARP Diet and Health cohort. Environ Health Perspect 124:484-490; http://dx.doi.org/10.1289/ehp.1509676.

Background: Clinical studies have shown that exposure to fine particulate matter (PM2.5) can increase insulin resistance and blood pressure. The epidemiologic evidence for an association of PM2.5 exposure with the incidence of type 2 diabetes or hypertension is inconsistent. Even a modest association would have great public health importance given the ubiquity of exposure and high prevalence of the conditions., Methods: We used Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident type 2 diabetes and hypertension associated with exposure to PM2.5 in a large cohort of African American women living in 56 metropolitan areas across the US, using data from the Black Women's Health Study. Pollutant levels were estimated at all residential locations over follow-up with a hybrid model incorporating land use regression and Bayesian Maximum Entropy techniques., Results: During 1995 to 2011, 4,387 cases of diabetes and 9,570 cases of hypertension occurred. In models controlling for age, questionnaire cycle, and metro area, there were positive associations with diabetes (HR = 1.13, 95% CI = 1.04, 1.24) and hypertension (HR = 1.06, 95% CI = 1.00, 1.12) per interquartile range of PM2.5 (2.9 μg/m). Multivariable HRs, however, were 0.99 (95% CI = 0.90, 1.09) for diabetes and 0.99 (95% CI = 0.93, 1.06) for hypertension., Conclusions: Our results provide little support for an association of PM2.5 with diabetes or hypertension incidence.

Background: Numerous studies have examined associations between air pollution and pregnancy outcomes, but most have been restricted to urban populations living near monitors., Objectives: We examined the association between pregnancy outcomes and fine particulate matter in a large national study including urban and rural areas., Methods: Analyses were based on approximately 3 million singleton live births in Canada between 1999 and 2008. Exposures to PM2.5 (particles of median aerodynamic diameter ≤ 2.5 μm) were assigned by mapping the mother's postal code to a monthly surface based on a national land use regression model that incorporated observations from fixed-site monitoring stations and satellite-derived estimates of PM2.5. Generalized estimating equations were used to examine the association between PM2.5 and preterm birth (gestational age < 37 weeks), term low birth weight (< 2,500 g), small for gestational age (SGA; < 10th percentile of birth weight for gestational age), and term birth weight, adjusting for individual covariates and neighborhood socioeconomic status (SES)., Results: In fully adjusted models, a 10-μg/m(3) increase in PM2.5 over the entire pregnancy was associated with SGA (odds ratio = 1.04; 95% CI 1.01, 1.07) and reduced term birth weight (-20.5 g; 95% CI -24.7, -16.4). Associations varied across subgroups based on maternal place of birth and period (1999-2003 vs. 2004-2008)., Conclusions: This study, based on approximately 3 million births across Canada and employing PM2.5 estimates from a national spatiotemporal model, provides further evidence linking PM2.5 and pregnancy outcomes.

Background: Few studies examining the associations between long-term exposure to ambient air pollution and mortality have considered multiple pollutants when assessing changes in exposure due to residential mobility during follow-up., Objective: We investigated associations between cause-specific mortality and ambient concentrations of fine particulate matter (≤ 2.5 μm; PM2.5), ozone (O3), and nitrogen dioxide (NO2) in a national cohort of about 2.5 million Canadians., Methods: We assigned estimates of annual concentrations of these pollutants to the residential postal codes of subjects for each year during 16 years of follow-up. Historical tax data allowed us to track subjects' residential postal code annually. We estimated hazard ratios (HRs) for each pollutant separately and adjusted for the other pollutants. We also estimated the product of the three HRs as a measure of the cumulative association with mortality for several causes of death for an increment of the mean minus the 5th percentile of each pollutant: 5.0 μg/m3 for PM2.5, 9.5 ppb for O3, and 8.1 ppb for NO2., Results: PM2.5, O3, and NO2 were associated with nonaccidental and cause-specific mortality in single-pollutant models. Exposure to PM2.5 alone was not sufficient to fully characterize the toxicity of the atmospheric mix or to fully explain the risk of mortality associated with exposure to ambient pollution. Assuming additive associations, the estimated HR for nonaccidental mortality corresponding to a change in exposure from the mean to the 5th percentile for all three pollutants together was 1.075 (95% CI: 1.067, 1.084). Accounting for residential mobility had only a limited impact on the association between mortality and PM2.5 and O3, but increased associations with NO2., Conclusions: In this large, national-level cohort, we found positive associations between several common causes of death and exposure to PM2.5, O3, and NO2., Citation: Crouse DL, Peters PA, Hystad P, Brook JR, van Donkelaar A, Martin RV, Villeneuve PJ, Jerrett M, Goldberg MS, Pope CA III, Brauer M, Brook RD, Robichaud A, Menard R, Burnett RT. 2015. Ambient PM2.5, O3, and NO2 exposures and associations with mortality over 16 years of follow-up in the Canadian Census Health and Environment Cohort (CanCHEC). Environ Health Perspect 123:1180-1186; http://dx.doi.org/10.1289/ehp.1409276.

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

Rationale: Growing evidence suggests that long-term exposure to fine particulate matter (PM2.5) air pollution contributes to risk of cardiovascular disease (CVD) morbidity and mortality. There is uncertainty about who are most susceptible. Individuals with underlying cardiometabolic disorders, including hypertension, diabetes mellitus, and obesity, may be at greater risk. PM2.5 pollution may also contribute to cardiometabolic disorders, augmenting CVD risk., Objective: This analysis evaluates relationships between long-term PM2.5 exposure and cardiometabolic disease on risk of death from CVD and cardiometabolic conditions., Methods and Results: Data on 669 046 participants from the American Cancer Society Cancer Prevention Study II cohort were linked to modeled PM2.5 concentrations at geocoded home addresses. Cox proportional hazards regression models were used to estimate adjusted hazards ratios for death from CVD and cardiometabolic diseases based on death-certificate information. Effect modification by pre-existing cardiometabolic risk factors on the PM2.5-CVD mortality association was examined. PM2.5 exposure was associated with CVD mortality, with the hazards ratios (95% confidence interval) per 10 μg/m(3) increase in PM2.5 equal to 1.12 (1.10-1.15). Deaths linked to hypertension and diabetes mellitus (mentioned on death certificate as either primary or contributing cause of death) were also associated with PM2.5. There was no consistent evidence of effect modification by cardiometabolic disease risk factors on the PM2.5-CVD mortality association., Conclusions: Pollution-induced CVD mortality risk is observed for those with and without existing cardiometabolic disorders. Long-term exposure may also contribute to the development or exacerbation of cardiometabolic disorders, increasing risk of CVD, and cardiometabolic disease mortality., (© 2014 American Heart Association, Inc.)

Background: Laboratory studies suggest that exposure to fine particulate matter (≤2.5 μm in diameter) (PM2.5) can trigger a combination of pathophysiological responses that may induce the development of hypertension. However, epidemiological evidence relating PM2.5 and hypertension is sparse. We thus conducted a population-based cohort study to determine whether exposure to ambient PM2.5 is associated with incident hypertension., Methods and Results: We assembled a cohort of 35 303 nonhypertensive adults from Ontario, Canada, who responded to 1 of 4 population-based health surveys between 1996 and 2005 and were followed up until December 31, 2010. Incident diagnoses of hypertension were ascertained from the Ontario Hypertension Database, a validated registry of persons diagnosed with hypertension in Ontario (sensitivity=72%, specificity=95%). Estimates of long-term exposure to PM2.5 at participants' postal-code residences were derived from satellite observations. We used Cox proportional hazards models, adjusting for various individual and contextual risk factors including body mass index, smoking, physical activity, and neighbourhood-level unemployment rates. We conducted various sensitivity analyses to assess the robustness of the effect estimate, such as investigating several time windows of exposure and controlling for potential changes in the risk of hypertension over time. Between 1996 and 2010, we identified 8649 incident cases of hypertension and 2296 deaths. For every 10-µg/m(3) increase of PM2.5, the adjusted hazard ratio of incident hypertension was 1.13 (95% confidence interval, 1.05-1.22). Estimated associations were comparable among all sensitivity analyses., Conclusions: This study supports an association between PM2.5 and incident hypertension.

Objective: Recent studies suggest that chronic exposure to air pollution can promote the development of diabetes. However, whether this relationship actually translates into an increased risk of mortality attributable to diabetes is uncertain., Research Design and Methods: We evaluated the association between long-term exposure to ambient fine particulate matter (PM2.5) and diabetes-related mortality in a prospective cohort analysis of 2.1 million adults from the 1991 Canadian census mortality follow-up study. Mortality information, including ∼5,200 deaths coded as diabetes being the underlying cause, was ascertained by linkage to the Canadian Mortality Database from 1991 to 2001. Subject-level estimates of long-term exposure to PM2.5 were derived from satellite observations. The hazard ratios (HRs) for diabetes-related mortality were related to PM2.5 and adjusted for individual-level and contextual variables using Cox proportional hazards survival models., Results: Mean PM2.5 exposure levels for the entire population were low (8.7 µg/m3; SD, 3.9 µg/m3; interquartile range, 6.2 µg/m3). In fully adjusted models, a 10-µg/m3 elevation in PM2.5 exposure was associated with an increase in risk for diabetes-related mortality (HR, 1.49; 95% CI, 1.37-1.62). The monotonic change in risk to the population persisted to PM2.5 concentration<5 µg/m3., Conclusions: Long-term exposure to PM2.5, even at low levels, is related to an increased risk of mortality attributable to diabetes. These findings have considerable public health importance given the billions of people exposed to air pollution and the worldwide growing epidemic of diabetes.

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.

Background: Laboratory studies suggest that fine particulate matter (≤ 2.5 µm in diameter; PM(2.5)) can activate pathophysiological responses that may induce insulin resistance and type 2 diabetes. However, epidemiological evidence relating PM2.5 and diabetes is sparse, particularly for incident diabetes., Objectives: We conducted a population-based cohort study to determine whether long-term exposure to ambient PM(2.5) is associated with incident diabetes., Methods: We assembled a cohort of 62,012 nondiabetic adults who lived in Ontario, Canada, and completed one of five population-based health surveys between 1996 and 2005. Follow-up extended until 31 December 2010. Incident diabetes diagnosed between 1996 and 2010 was ascertained using the Ontario Diabetes Database, a validated registry of persons diagnosed with diabetes (sensitivity = 86%, specificity = 97%). Six-year average concentrations of PM2.5 at the postal codes of baseline residences were derived from satellite observations. We used Cox proportional hazards models to estimate the associations, adjusting for various individual-level risk factors and contextual covariates such as smoking, body mass index, physical activity, and neighborhood-level household income. We also conducted multiple sensitivity analyses. In addition, we examined effect modification for selected comorbidities and sociodemographic characteristics., Results: There were 6,310 incident cases of diabetes over 484,644 total person-years of follow-up. The adjusted hazard ratio for a 10-µg/m(3) increase in PM(2.5) was 1.11 (95% CI: 1.02, 1.21). Estimated associations were comparable among all sensitivity analyses. We did not find strong evidence of effect modification by comorbidities or sociodemographic covariates., Conclusions: This study suggests that long-term exposure to PM2.5 may contribute to the development of diabetes.

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

Background: A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data., Objective: We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation., Methods: We developed a space-time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals., Results: The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates., Conclusions: We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

Background: Evidence suggests that longer-term exposure to air pollutants over years confers higher risks of cardiovascular morbidity and mortality than shorter-term exposure. One explanation is that the cumulative adverse effects that develop over longer durations lead to the genesis of chronic disease. Preliminary epidemiological and clinical evidence suggests that air pollution may contribute to the development of hypertension and type 2 diabetes mellitus., Methods and Results: We used Cox proportional hazards models to assess incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for incident hypertension and diabetes mellitus associated with exposure to fine particulate matter (PM(2.5)) and nitrogen oxides in a cohort of black women living in Los Angeles. Pollutant levels were estimated at participants' residential addresses with land use regression models (nitrogen oxides) and interpolation from monitoring station measurements (PM(2.5)). Over follow-up from 1995 to 2005, 531 incident cases of hypertension and 183 incident cases of diabetes mellitus occurred. When pollutants were analyzed separately, the IRR for hypertension for a 10-μg/m(3) increase in PM(2.5) was 1.48 (95% CI, 0.95-2.31), and the IRR for the interquartile range (12.4 parts per billion) of nitrogen oxides was 1.14 (95% CI, 1.03-1.25). The corresponding IRRs for diabetes mellitus were 1.63 (95% CI, 0.78-3.44) and 1.25 (95% CI, 1.07-1.46). When both pollutants were included in the same model, the IRRs for PM(2.5) were attenuated and the IRRs for nitrogen oxides were essentially unchanged for both outcomes., Conclusion: Our results suggest that exposure to air pollutants, especially traffic-related pollutants, may increase the risk of type 2 diabetes mellitus and possibly of hypertension.

Increasing evidence links air pollution to the risk of cardiovascular disease. This study investigated the association between ischemic heart disease (IHD) prevalence and exposure to traffic-related air pollution (nitrogen dioxide [NO₂], fine particulate matter [PM₂.₅], and ozone [O₃]) in a population of susceptible subjects in Toronto. Local (NO₂) exposures were modeled using land use regression based on extensive field monitoring. Regional exposures (PM₂.₅, O₃) were modeled as confounders using inverse distance weighted interpolation based on government monitoring data. The study sample consisted of 2360 patients referred during 1992 to 1999 to a pulmonary clinic at the Toronto Western Hospital in Toronto, Ontario, Canada, to diagnose or manage a respiratory complaint. IHD status was determined by clinical database linkages (ICD-9-CM 412-414). The association between IHD and air pollutants was assessed with a modified Poisson regression resulting in relative risk estimates. Confounding was controlled with individual and neighborhood-level covariates. After adjusting for multiple covariates, NO₂ was significantly associated with increased IHD risk, relative risk (RR) = 1.33 (95% confidence interval [CI]: 1.2, 1.47). Subjects living near major roads and highways had a trend toward an elevated risk of IHD, RR = 1.08 (95% CI: 0.99, 1.18). Regional PM₂.₅ and O₃ were not associated with risk of IHD.

Background: Lung cancer and cardiovascular disease (CVD) mortality risks increase with smoking, secondhand smoke (SHS), and exposure to fine particulate matter < 2.5 μm in diameter (PM₂.₅) from ambient air pollution. Recent research indicates that the exposure-response relationship for CVD is nonlinear, with a steep increase in risk at low exposures and flattening out at higher exposures. Comparable estimates of the exposure-response relationship for lung cancer are required for disease burden estimates and related public health policy assessments., Objectives: We compared exposure-response relationships of PM₂.₅ with lung cancer and cardiovascular mortality and considered the implications of the observed differences for efforts to estimate the disease burden of PM2.5., Methods: Prospective cohort data for 1.2 million adults were collected by the American Cancer Society as part of the Cancer Prevention Study II. We estimated relative risks (RRs) for increments of cigarette smoking, adjusting for various individual risk factors. RRs were plotted against estimated daily dose of PM₂.₅ from smoking along with comparison estimates for ambient air pollution and SHS., Results: For lung cancer mortality, excess risk rose nearly linearly, reaching maximum RRs > 40 among long-term heavy smokers. Excess risks for CVD mortality increased steeply at low exposure levels and leveled off at higher exposures, reaching RRs of approximately 2-3 for cigarette smoking., Conclusions: The exposure-response relationship associated with PM₂.₅ is qualitatively different for lung cancer versus cardiovascular mortality. At low exposure levels, cardiovascular deaths are projected to account for most of the burden of disease, whereas at high levels of PM₂.₅, lung cancer becomes proportionately more important.

Rationale: Several studies have linked long-term exposure to particulate air pollution with increased cardiopulmonary mortality; only two have also examined incident circulatory disease., Objectives: To examine associations of individualized long-term exposures to particulate and gaseous air pollution with incident myocardial infarction and stroke, as well as all-cause and cause specific mortality., Methods: We estimated long-term residential air pollution exposure for more than 100,000 participants in the California Teachers Study, a prospective cohort of female public school professionals.We linked geocoded residential addresses with inverse distance-weighted monthly pollutant surfaces for two measures of particulate matter and for several gaseous pollutants. We examined associations between exposure to these pollutants and risks of incident myocardial infarction and stroke, and of all-cause and cause-specific mortality, using Cox proportional hazards models., Measurements and Main Results: We found elevated hazard ratios linking long-term exposure to particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5), scaled to an increment of 10 μg/m3 with mortality from ischemic heart disease (IHD) (1.20; 95% confidence interval [CI], 1.02-1.41) and, particularly among postmenopausal women, incident stroke (1.19; 95% CI, 1.02-1.38). Long-term exposure to particulate matter less than 10 μm in aerodynamic diameter (PM10) was associated with elevated risks for IHD mortality (1.06; 95% CI, 0.99-1.14) and incident stroke (1.06; 95% CI, 1.00-1.13), while exposure to nitrogen oxides was associated with elevated risks for IHD and all cardiovascular mortality., Conclusions: This study provides evidence linking long-term exposure to PM2.5 and PM10 with increased risks of incident stroke as well as IHD mortality; exposure to nitrogen oxides was also related to death from cardiovascular diseases.

Little is known about the levels of air pollution at public parks where regular exercise takes place or in park-adjacent neighborhoods where people have easy access to parks. In this study we investigated the ambient concentrations of criteria pollutants nitrogen dioxide (NO(2)), fine particulate (PM(2.5)) and ozone (O(3)) at public parks and in park-adjacent neighborhoods for metropolitan Los Angeles. Socioeconomic and racial-ethnic inequalities in exposure to the three criteria pollutants were also investigated using multiple linear regression models. In addition, differences in inhalation doses from breathing the three +criteria pollutants were investigated for the top and bottom quartile racial composition in the parks and neighborhoods. Our research showed that although public parks had on average the lowest pollutant concentrations of NO(2) and PM(2.5), they had relatively high O(3) concentrations. Park-adjacent neighborhoods, by contrast, had the highest NO(2) and PM(2.5) concentrations, but the lowest O(3) concentrations. Higher exposures to NO(2) and PM(2.5) were systematically identified for the lower socioeconomic position or higher minority population neighborhoods. For children and adolescents aged 6-15 engaging in high and moderate intensity activities in and around public parks, those from the top quartile of primarily Hispanic neighborhoods had much higher (63%) inhaled doses of NO(2) compared to the bottom quartile counterpart. PM(2.5) showed a similar but less pronounced pattern of inhalation doses. Evidence of socioeconomic and racial-ethnic gradients was found in air pollution exposure and inhalation doses in and around the urban parks in Los Angeles. This suggests that patterns of exposure inequality found in other environmental justice research are present in exposures in and around urban parks., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Rationale: Little is known about the long-term effects of air pollution on pneumonia hospitalization in the elderly., Objectives: To assess the effect of long-term exposure to ambient nitrogen dioxide, sulfur dioxide, and fine particulate matter with diameter equal to or smaller than 2.5 microm (PM(2.5)) on hospitalization for community-acquired pneumonia in older adults., Methods: We used a population-based case-control study in Hamilton, Ontario, Canada. We enrolled 345 hospitalized patients aged 65 years or more for community-acquired pneumonia and 494 control participants, aged 65 years and more, randomly selected from the same community as cases from July 2003 to April 2005. Health data were collected by personal interview. Annual average levels of nitrogen dioxide, sulfur dioxide, and PM(2.5) before the study period were estimated at the residential addresses of participants by inverse distance weighting, bicubic splined and land use regression methods and merged with participants' health data., Measurements and Main Results: Long-term exposure to higher levels of nitrogen dioxide and PM(2.5) was significantly associated with hospitalization for community-acquired pneumonia (odds ratio [OR], 2.30; 95% confidence interval [CI], 1.25 to 4.21; P = 0.007 and OR, 2.26; 95% CI, 1.20 to 4.24; P = 0.012, respectively, over the 5th-95th percentile range increase of exposure). Sulfur dioxide did not appear to have any association (OR, 0.97; 95% CI, 0.59 to 1.61; P = 0.918). Results were somewhat sensitive to the choice of methods used to estimate air pollutant levels at residential addresses, although all risks from nitrogen dioxide and PM(2.5) exposure were positive and generally significant., Conclusions: In older adults, exposure to ambient nitrogen dioxide and PM(2.5) was associated with hospitalization for community-acquired pneumonia.

Background: Fine particulate matter exposure from both ambient air pollution and secondhand cigarette smoke has been associated with larger risks of cardiovascular mortality than would be expected on the basis of linear extrapolations of the relative risks from active smoking. This study directly assessed the shape of the exposure-response relationship between cardiovascular mortality and fine particulates from cigarette smoke and ambient air pollution., Methods and Results: Prospective cohort data for >1 million adults were collected by the American Cancer Society as part of the Cancer Prevention Study II in 1982. Cox proportional hazards regression models that included variables for increments of cigarette smoking and variables to control for education, marital status, body mass, alcohol consumption, occupational exposures, and diet were used to describe the mortality experience of the cohort. Adjusted relative risks of mortality were plotted against estimated average daily dose of fine particulate matter from cigarette smoke along with comparison estimates for secondhand cigarette smoke and air pollution. There were substantially increased cardiovascular mortality risks at very low levels of active cigarette smoking and smaller but significant excess risks even at the much lower exposure levels associated with secondhand cigarette smoke and ambient air pollution., Conclusions: Relatively low levels of fine particulate exposure from either air pollution or secondhand cigarette smoke are sufficient to induce adverse biological responses increasing the risk of cardiovascular disease mortality. The exposure-response relationship between cardiovascular disease mortality and fine particulate matter is relatively steep at low levels of exposure and flattens out at higher exposures.

Land use regression (LUR) has emerged as an effective means of estimating exposure to air pollution in epidemiological studies. We created the first LUR models of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOX) for the complex megalopolis of Los Angeles (LA), California. Two-hundred and one sampling sites (the largest sampling design to date for LUR estimation) for two seasons were selected using a location-allocation algorithm that maximized the potential variability in measured pollutant concentrations and represented populations in the health study. Traffic volumes, truck routes and road networks, land use data, satellite-derived vegetation greenness and soil brightness, and truck route slope gradients were used for predicting NOX concentrations. A novel model selection strategy known as "ADDRESS" (A Distance Decay REgression Selection Strategy) was used to select optimized buffer distances for potential predictor variables and maximize model performance. Final regression models explained 81%, 86% and 85% of the variance in measured NO, NO2 and NOX concentrations, respectively. Cross-validation analyses suggested a prediction accuracy of 87-91%. Remote sensing-derived variables were significantly correlated with NOX concentrations, suggesting these data are useful surrogates for modeling traffic-related pollution when certain land use data are unavailable. Our study also demonstrated that reactive pollutants such as NO and NO2 could have high spatial extents of influence (e.g., > 5000 m from expressway) and high background concentrations in certain geographic areas. This paper represents the first attempt to model traffic-related air pollutants at a fine scale within such a complex and large urban region.

We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) cohort in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities. The current study sought to clarify outstanding scientific issues that arose from our earlier HEI-sponsored Reanalysis of the original ACS study data (the Particle Epidemiology Reanalysis Project). Specifically, we examined (1) how ecologic covariates at the community and neighborhood levels might confound and modify the air pollution-mortality association; (2) how spatial autocorrelation and multiple levels of data (e.g., individual and neighborhood) can be taken into account within the random effects Cox model; (3) how using land-use regression to refine measurements of air pollution exposure to the within-city (or intra-urban) scale might affect the size and significance of health effects in the Los Angeles and New York City regions; and (4) what exposure time windows may be most critical to the air pollution-mortality association. The 18 years of follow-up (extended from 7 years in the original study [Pope et al. 1995]) included vital status data for the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through December 31, 2000 and more recent exposure data from air pollution monitoring sites for the metropolitan areas. In the Nationwide Analysis, the influence of ecologic covariate data (such as education attainment, housing characteristics, and level of income; data obtained from the 1980 U.S. Census; see Ecologic Covariates sidebar on page 14) on the air pollution-mortality association were examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference between each ZCA value and the MSA value (DIFF). In contrast to previous analyses that did not directly include ecologic covariates at the ZCA scale, risk estimates increased when ecologic covariates were included at all scales. The ecologic covariates exerted their greatest effect on mortality from ischemic heart disease (IHD), which was also the health outcome most strongly related with exposure to PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), sulfate (SO4(2-)), and sulfur dioxide (SO2), and the only outcome significantly associated with exposure to nitrogen dioxide (NO2). When ecologic covariates were simultaneously included at both the MSA and DIFF levels, the hazard ratio (HR) for mortality from IHD associated with PM2.5 exposure (average concentration for 1999-2000) increased by 7.5% and that associated with SO4(2-) exposure (average concentration for 1990) increased by 12.8%. The two covariates found to exert the greatest confounding influence on the PM2.5-mortality association were the percentage of the population with a grade 12 education and the median household income. Also in the Nationwide Analysis, complex spatial patterns in the CPS-II data were explored with an extended random effects Cox model (see Glossary of Statistical Terms at end of report) that is capable of clustering up to two geographic levels of data. Using this model tended to increase the HR estimate for exposure to air pollution and also to inflate the uncertainty in the estimates. Including ecologic covariates decreased the variance of the results at both the MSA and ZCA scales; the largest decrease was in residual variation based on models in which the MSA and DIFF levels of data were included together, which suggests that partitioning the ecologic covariates into between-MSA and within-MSA values more completely captures the sources of variation in the relationship between air pollution, ecologic covariates, and mortality. Intra-Urban Analyses were conducted for the New York City and Los Angeles regions. The results of the Los Angeles spatial analysis, where we found high exposure contrasts within the Los Angeles region, showed that air pollution-mortality risks were nearly 3 times greater than those reported from earlier analyses. This suggests that chronic health effects associated with intra-urban gradients in exposure to PM2.5 may be even larger between ZCAs within an MSA than the associations between MSAs that have been previously reported. However, in the New York City spatial analysis, where we found very little exposure contrast between ZCAs within the New York region, mortality from all causes, cardiopulmonary disease (CPD), and lung cancer was not elevated. A positive association was seen for PM2.5 exposure and IHD, which provides evidence of a specific association with a cause of death that has high biologic plausibility. These results were robust when analyses controlled (1) the 44 individual-level covariates (from the ACS enrollment questionnaire in 1982; see 44 Individual-Level Covariates sidebar on page 22) and (2) spatial clustering using the random effects Cox model. Effects were mildly lower when unemployment at the ZCA scale was included. To examine whether there is a critical exposure time window that is primarily responsible for the increased mortality associated with ambient air pollution, we constructed individual time-dependent exposure profiles for particulate and gaseous air pollutants (PM2.5 and SO2) for a subset of the ACS CPS-II participants for whom residence histories were available. The relevance of the three exposure time windows we considered was gauged using the magnitude of the relative risk (HR) of mortality as well as the Akaike information criterion (AIC), which measures the goodness of fit of the model to the data. For PM2.5, no one exposure time window stood out as demonstrating the greatest HR; nor was there any clear pattern of a trend in HR going from recent to more distant windows or vice versa. Differences in AIC values among the three exposure time windows were also small. The HRs for mortality associated with exposure to SO2 were highest in the most recent time window (1 to 5 years), although none of these HRs were significantly elevated. Identifying critical exposure time windows remains a challenge that warrants further work with other relevant data sets. This study provides additional support toward developing cost-effective air quality management policies and strategies. The epidemiologic results reported here are consistent with those from other population-based studies, which collectively have strongly supported the hypothesis that long-term exposure to PM2.5 increases mortality in the general population. Future research using the extended Cox-Poisson random effects methods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will provide additional insight.

Background: Long-term human exposure to ambient pollutants can be an important contributing or etiologic factor of many chronic diseases. Spatiotemporal estimation (mapping) of long-term exposure at residential areas based on field observations recorded in the U.S. Environmental Protection Agency's Air Quality System often suffer from missing data issues due to the scarce monitoring network across space and the inconsistent recording periods at different monitors., Objective: We developed and compared two upscaling methods: UM1 (data aggregation followed by exposure estimation) and UM2 (exposure estimation followed by data aggregation) for the long-term PM10 (particulate matter with aerodynamic diameter < or = 10 microm) and ozone exposure estimations and applied them in multiple time scales to estimate PM and ozone exposures for the residential areas of the Health Effects of Air Pollution on Lupus (HEAPL) study., Method: We used Bayesian maximum entropy (BME) analysis for the two upscaling methods. We performed spatiotemporal cross-validations at multiple time scales by UM1 and UM2 to assess the estimation accuracy across space and time., Results: Compared with the kriging method, the integration of soft information by the BME method can effectively increase the estimation accuracy for both pollutants. The spatiotemporal distributions of estimation errors from UM1 and UM2 were similar. The cross-validation results indicated that UM2 is generally better than UM1 in exposure estimations at multiple time scales in terms of predictive accuracy and lack of bias. For yearly PM10 estimations, both approaches have comparable performance, but the implementation of UM1 is associated with much lower computation burden., Conclusion: BME-based upscaling methods UM1 and UM2 can assimilate core and site-specific knowledge bases of different formats for long-term exposure estimation. This study shows that UM1 can perform reasonably well when the aggregation process does not alter the spatiotemporal structure of the original data set; otherwise, UM2 is preferable.

Background: Although many studies have linked elevations in tropospheric ozone to adverse health outcomes, the effect of long-term exposure to ozone on air pollution-related mortality remains uncertain. We examined the potential contribution of exposure to ozone to the risk of death from cardiopulmonary causes and specifically to death from respiratory causes., Methods: Data from the study cohort of the American Cancer Society Cancer Prevention Study II were correlated with air-pollution data from 96 metropolitan statistical areas in the United States. Data were analyzed from 448,850 subjects, with 118,777 deaths in an 18-year follow-up period. Data on daily maximum ozone concentrations were obtained from April 1 to September 30 for the years 1977 through 2000. Data on concentrations of fine particulate matter (particles that are < or = 2.5 microm in aerodynamic diameter [PM(2.5)]) were obtained for the years 1999 and 2000. Associations between ozone concentrations and the risk of death were evaluated with the use of standard and multilevel Cox regression models., Results: In single-pollutant models, increased concentrations of either PM(2.5) or ozone were significantly associated with an increased risk of death from cardiopulmonary causes. In two-pollutant models, PM(2.5) was associated with the risk of death from cardiovascular causes, whereas ozone was associated with the risk of death from respiratory causes. The estimated relative risk of death from respiratory causes that was associated with an increment in ozone concentration of 10 ppb was 1.040 (95% confidence interval, 1.010 to 1.067). The association of ozone with the risk of death from respiratory causes was insensitive to adjustment for confounders and to the type of statistical model used., Conclusions: In this large study, we were not able to detect an effect of ozone on the risk of death from cardiovascular causes when the concentration of PM(2.5) was taken into account. We did, however, demonstrate a significant increase in the risk of death from respiratory causes in association with an increase in ozone concentration., (2009 Massachusetts Medical Society)

Evidence is increasing that long-term exposure to ambient air pollution is associated with deaths from cardiopulmonary diseases. In a 2002 pilot study, we reported clear indications that traffic-related air pollution, especially at the local scale, was related to cardiopulmonary mortality in a randomly selected subcohort of 5000 older adults participating in the ongoing Netherlands Cohort Study (NLCS) on diet and cancer. In the current study, referred to as NLCS-AIR, our objective was to obtain more precise estimates of the effects of traffic-related air pollution by analyzing associations with cause-specific mortality, as well as lung cancer incidence, in the full cohort of approximately 120,000 subjects. Cohort members were 55 to 69 years of age at enrollment in 1986. Follow-up was from 1987 through 1996 for mortality (17,674 deaths) and from late 1986 through 1997 for lung cancer incidence (2234 cases). Information about potential confounding variables and effect modifiers was available from the questionnaire that subjects completed at enrollment and from publicly available data (including neighborhood-scale information such as income distributions). The NLCS was designed for a case-cohort approach, which makes use of all the cases in the full cohort, while data for the random subcohort are used to estimate person-time experience in the study. Full information on confounders was available for the subjects in the random subcohort and for the emerging cases of mortality and lung cancer incidence during the follow-up period, and in NLCS-AIR we used the case-cohort approach to examine the relation between exposure to air pollution and cause-specific mortality and lung cancer. We also specified a standard Cox proportional hazards model within the full cohort, for which information on potential confounding variables was much more limited. Exposure to air pollution was estimated for the subjects' home addresses at baseline in 1986. Concentrations were estimated for black smoke (a simple marker for soot) and nitrogen dioxide (NO2) as indicators of traffic-related air pollution, as well as nitric oxide (NO), sulfur dioxide (SO2), and particulate matter with aerodynamic diameter < or = 2.5 microm (PM2.5), as estimated from measurements of particulate matter with aerodynamic diameter < or = 10 microm (PM10). Overall long-term exposure concentrations were considered to be a function of air pollution contributions at regional, urban, and local scales. We used interpolation from data obtained routinely at regional stations of the National Air Quality Monitoring Network (NAQMN) to estimate the regional component of exposure at the home address. Average pollutant concentrations were estimated from NAQMN measurements for the period 1976 through 1996. Land-use regression methods were used to estimate the urban exposure component. For the local exposure component, geographic information systems (GISs) were used to generate indicators of traffic exposure that included traffic intensity on and distance to nearby roads. A major effort was made to collect traffic intensity data from individual municipalities. The exposure variables were refined considerably from those used in the pilot study, but we also analyzed the data for the full cohort in the current study using the exposure indicators of the pilot study. We analyzed the data in models with the estimated overall pollutant concentration as a single variable and with the background concentration (the sum of regional and urban components) and the local exposure estimate from traffic indicators as separate variables. In the full-cohort analyses adjusted for the limited set of confounders, estimated overall exposure concentrations of black smoke, NO2, NO, and PM2.5 were associated with mortality. For a 10-microg/m3 increase in the black smoke concentration, the relative risk (RR) (95% confidence interval [CI]) was 1.05 (1.00-1.11) for natural-cause (nonaccidental) mortality, 1.04 (0.95-1.13) for cardiovascular mortality, 1.22 (0.99-1.50) for respiratory mortality, 1.03 (0.88-1.20) for lung cancer mortality, and 1.04 (0.97-1.12) for noncardiopulmonary, non-lung cancer mortality. Results were similar for NO2, NO, and PM2.5. For a 10-microg/m3 increase in PM2.5 concentration, the RR for natural-cause mortality was 1.06 (95% CI, 0.97-1.16), the same as in the results of the American Cancer Society Study reported by Pope and colleagues in 2002. The highest relative risks were found for respiratory mortality, though confidence intervals were wider for this less-frequent cause of death. No associations with mortality were found for SO2. Some of the associations between the traffic indicator variables used to assess traffic intensity near the home and mortality reached statistical significance in the full cohort. For an increase in traffic intensity of 10,000 motor vehicles in 24 hours (motor vehicles/day) on the road nearest a subject's residence, the RR was 1.03 (95% CI, 1.00-1.08) for natural-cause mortality, 1.05 (0.99-1.12) for cardiovascular mortality, 1.10 (0.95-1.26) for respiratory mortality, 1.07 (0.96-1.19) for lung cancer mortality, and 1.00 (0.94-1.06) for noncardiopulmonary, non-lung cancer mortality. Results were similar for traffic intensity in a 100-m buffer around the subject's residence and living near a major road (a road with more than 10,000 motor vehicles/day). Distance in meters to the nearest major road and traffic intensity on the nearest major road were not associated with any of the mortality outcomes. We did not find an association between cardiopulmonary mortality and living near a major road as defined using the methods of the pilot study. In the case-cohort analyses adjusted for all potential confounders, we found no associations between background air pollution and mortality. The associations between traffic intensity and mortality were weaker than in the full cohort, and confidence intervals were wider, consistent with the smaller number of subjects. The lower relative risks of mortality associated with traffic variables in the case-cohort study population could be related to the particular subcohort that was randomly selected from the full cohort, as the risks estimated with the actual subcohort were well below the average estimates obtained for 100 new case-cohort analyses with 100 alternative subcohorts of 5000 subjects each that we randomly selected from the full cohort. Differences in adjusted relative risks between the full-cohort and the case-cohort analyses could be explained by random error introduced by sampling from the full cohort and by a selection effect resulting from the relatively large number of missing data for variables in the extensive confounder model used in the case-cohort analyses. More complete control for confounding probably did not contribute much to the lower relative risks in the case-cohort analyses, especially for the traffic variables, as results were similar when the limited confounder model for the full cohort was used in analyses of the subjects in the case-cohort study population. In additional analyses using black smoke concentrations as the exposure variables, we found that the association between overall black smoke and cardiopulmonary mortality was somewhat stronger for case-cohort subjects who did not change residence during follow-up, and in the full cohort, there was a tendency for relative risks to be higher for subjects living in the three major cities included in the study. Adjustment for estimated exposure to traffic noise did not affect the associations of background black smoke and traffic intensity with cardiovascular mortality. There was some indication of an association between traffic noise and cardiovascular mortality only for the 1.6% of the subjects in the full cohort who were exposed to traffic noise in the highest category of > 65 A-weighted decibels (dB(A); decibels with the sound pressure scale adjusted to conform with the frequency response of the human ear). Examination of sex, smoking status, educational level, and vegetable and fruit intake as possible effect modifiers showed that for overall black smoke concentrations, associations with mortality tended to be stronger in case-cohort subjects with lower levels of education and those with low fruit intake, but differences between strata were not statistically significant. For lung cancer incidence, we found essentially no relation to exposure to NO2, black smoke, PM2.5, SO2, or several traffic indicators. Associations of overall air pollution concentrations and traffic indicator variables with lung cancer incidence were, however, found in subjects who had never smoked, with an RR of 1.47 (95% CI, 1.01-2.16) for a 10-microg/m3 increase in overall black smoke concentration. In the current study, the mortality risks associated with both background air pollution and traffic exposure variables were much smaller than the estimate previously reported in the pilot study for risk of cardiopulmonary mortality associated with living near a major road (RR, 1.95; 95% CI, 1.09-3.51). The differences are most likely due to the extension of the follow-up period in the current study and to random error in the pilot study related to sampling from the full cohort. Though relative risks were generally small in the current study, long-term average concentrations of black smoke, NO2, and PM2.5 were related to mortality, and associations of black smoke and NO2 exposure with natural-cause and respiratory mortality were statistically significant. Traffic intensity near the home was also related to natural-cause mortality. The highest relative risks associated with background air pollution and traffic variables were for respiratory mortality, though the number of deaths was smaller than for the other mortality categories. (ABSTRACT TRUNCATED)